Cell motility is fundamental process central to embryonic development, immune response, wound healing, angiogenesis, tissue engineering and various disease processes including cancer metastasis. Quantitative single cell motility assays provide a powerful tool to evaluate the impact of experimental treatments on targets of interest in these fields. Despite the steady improving price-performance and availability of automated, live cell imaging systems, quantitative individual cell motility assays continue to be a tedious, largely manual and inexact process. The lack of automation is primarily due to inadequate cell recognition capabilities in the start-of-the-art image informatics software. An easy to use image informatics tool for the accurate cell recognition and highly automated analyses of single cell motility assays would accelerate and expand quantitative cell motility studies, impacting many fields in basic research, drug discovery and disease related research. We are developing a next generation microscopy image informatics tool, SVCell, incorporating significantly improved cell recognition, measurement, and analysis technology for a broad range of cell types in fixed-point assays. We propose to extend SVCell's technology to enable accurate and highly automated image based individual cell motility assays. The specific aims are: 1) Extend SVCell to perform highly automated kinetic recognition of individual cells in time-lapse, phase contrast images; 2) Extend SVCell to enable the comprehensive and efficient analysis of kinetic measurements in an integrated analysis environment; and 3) Test extended SVCell in a well benchmarked cell motility experiment. The primary innovation is kinetic recognition that enables high recognition accuracy and tracking automation. A second innovation is comprehensive kinetic measurements within an integrated analysis environment that improves the quality and efficiency of motility assay outcomes. SVCell Motility will be one of a suite of open platform image informatics applications that we will market to the broad life sciences community on the SVCell platform. [unreadable] [unreadable]